Design and Evaluation of a Third-Order Regression Model for Estimating Glucose Levels from ECG Signal Features

Authors

DOI:

https://doi.org/10.61467/2007.1558.2026.v17i2.1110

Keywords:

Diabetes, Regression model, Anova, Clarke error grid

Abstract

Diabetes mellitus, also known simply as diabetes, is a chronic disease that affects the absorption of glucose from food, increasing levels in the blood. This disease mainly affects adults (type 2 diabetes) but can also occur in children (type 1 or childhood diabetes), as well as in pregnant women (gestational diabetes). Diabetes is one of the main diseases with the highest incidence and high mortality rate worldwide. Diabetes is a disease for which there is no cure; however, continuous monitoring and control of blood glucose levels reduce the risk of associated complications, such as gastrointestinal issues, vision loss, limb amputations (e.g., diabetic foot), and damage to vital organs such as the heart and kidneys. This article presents the results of the statistical and clinical evaluation for the estimation of blood glucose levels based on features derived from the ECG signal. The primary features used as estimating variables were heart rate, heart rate variability, amplitudes of the R, S, T waves, and the QT interval. For the electrocardiogram recording, lead VII was used.  The ANOVA analysis showed a result of 0. 0.7236.The clinical validation using the Clarke error grid showed a validation data percentage of 58.54% in zone A, 39.04% in zone B, 1.21% in zone C, and 1.21% in zone D, with a correlation of 0. 8525.

 

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References

Aggarwal, Y., Das, J., Mazumder, P. M., Kumar, R., & Sinha, R. K. (2021). Heart rate variability time domain features in automated prediction of diabetes in rat. Physical and Engineering Sciences in Medicine, 44(1), 45–52. https://doi.org/10.1007/s13246-020-00950-8

Agrawal, H., Jain, P., & Joshi, A. M. (2022). Machine learning models for non-invasive glucose measurement: Towards diabetes management in smart healthcare. Health and Technology, 12(5), 955–970. https://doi.org/10.1007/s12553-022-00690-7

Alsunaidi, B., Althobaiti, M., Tamal, M., Albaker, W., & Al-Naib, I. (2021). A review of non-invasive optical systems for continuous blood glucose monitoring. Sensors, 21(20), 6820. https://doi.org/10.3390/s21206820

Centers for Disease Control and Prevention. (n.d.). Diabetes basics. https://www.cdc.gov/diabetes/about/index.html

Clarke error grid analysis. (n.d.). MATLAB Central File Exchange. https://la.mathworks.com/matlabcentral/fileexchange/20545-clarke-error-grid-analysis

Drucker, D. J. (2018). Mechanisms of action and therapeutic application of glucagon. Cell Metabolism. https://doi.org/10.1016/j.cmet.2018.03.001

Elsayed, N. A., Aleppo, G., Aroda, V. R., Bannuru, R. R., Brown, F. M., Bruemmer, D., Collins, B. S., Hilliard, M. E., Isaacs, D., Johnson, E. L., Kahan, S., Khunti, K., Leon, J., Lyons, S. K., Perry, M. L., Prahalad, P., Pratley, R. E., Seley, J. J., Stanton, R. C., & Gabbay, R. A. (2023). Diabetes technology: Standards of care in diabetes—2023. Diabetes Care, 46(Suppl 1), S111–S127. https://doi.org/10.2337/DC23-S007

Elvebakk, O., Tronstad, C., Birkeland, K., & Jenssen, T. (2020). A multiparameter model for non-invasive detection of hypoglycemia. Biomedical Physics & Engineering Express. https://doi.org/10.1088/2057-1976/abe778

Galindo, R. J., Aleppo, G., Klonoff, D. C., Spanakis, E. K., Agarwal, S., Vellanki, P., Olson, D. E., Umpierrez, G. E., Davis, G. M., & Pasquel, F. J. (2020). Implementation of continuous glucose monitoring in the hospital. Journal of Diabetes Science and Technology, 14(4), 822–832. https://doi.org/10.1177/1932296820932903

International Diabetes Federation. (n.d.). Continuous glucose monitoring. https://idf.org/es/about-diabetes/continuous-glucose-monitoring/

International Diabetes Federation. (n.d.). Diabetes facts and figures. https://idf.org/es/about-diabetes/diabetes-facts-figures/

International Diabetes Federation. (n.d.). Type 1 diabetes. https://idf.org/es/about-diabetes/type-1-diabetes/

International Diabetes Federation. (n.d.). Type 2 diabetes. https://idf.org/es/about-diabetes/type-2-diabetes/

Jain, P., Joshi, A. M., Mohanty, S. P., & Cenkeramaddi, L. R. (2024). Non-invasive glucose measurement technologies: Recent advancements and future challenges. IEEE Access, 12, 61907–61936. https://doi.org/10.1109/ACCESS.2024.3389819

Jain, P., Pancholi, S., & Joshi, A. M. (2019). An IoMT based non-invasive precise blood glucose measurement system. In IEEE International Symposium on Smart Electronic Systems (pp. 111–116). https://doi.org/10.1109/iSES47678.2019.00034

Kajisa, T., Kuroi, T., Hara, H., & Sakai, T. (2024). Correlation analysis of heart rate variations and glucose fluctuations during sleep. Sleep Medicine, 113, 180–187. https://doi.org/10.1016/j.sleep.2023.11.038

Li, J., Tobore, I., Liu, Y., Kandwal, A., Wang, L., & Nie, Z. (2021). Non-invasive monitoring of three glucose ranges based on ECG. IEEE Journal of Biomedical and Health Informatics, 25(9), 3340–3350. https://doi.org/10.1109/JBHI.2021.3072628

Mardia, K. V., Kent, J. T., & Taylor, C. C. (1979). Multivariate analysis. Academic Press.

Naresh, M., Nagaraju, V. S., Kollem, S., Kumar, J., & Peddakrishna, S. (2024). Non-invasive glucose prediction using NIR technology with machine learning. Heliyon, 10(7), e28720. https://doi.org/10.1016/j.heliyon.2024.e28720

Okoye, K., & Hosseini, S. (2024). Analysis of variance (ANOVA) in R: One-way and two-way ANOVA. In R programming: Statistical data analysis in research (pp. 187–209). Springer. https://doi.org/10.1007/978-981-97-3385-9_9

Ramin, J., Mahyari, Z., Moulodi, M. J., Fatemi Ghiri, S. M., Tajalizadeh, H., Loloee Jahromi, A., Nakhostin, A., Abdollahifard, G., & Parsaei, H. (2021). An infrared non-invasive system for measuring blood glucose: A primary study using serum samples. Biomedical Physics & Engineering Express, 7(5). https://doi.org/10.1088/2057-1976/ac1c0b

Sun, X. (2022). Glucose detection through surface-enhanced Raman spectroscopy: A review. Analytica Chimica Acta, 1206, 339226. https://doi.org/10.1016/j.aca.2021.339226

Swapna, G., Soman, K. P., & Vinayakumar, R. (2018). Automated detection of diabetes using CNN. Procedia Computer Science, 132, 1253–1262. https://doi.org/10.1016/j.procs.2018.05.041

Swapna, G., Soman, K. P., & Vinayakumar, R. (2020). Diabetes detection using ECG signals: An overview. In Studies in Big Data. https://doi.org/10.1007/978-3-030-33966-1_14

Swapna, G., Vinayakumar, R., & Soman, K. P. (2018). Diabetes detection using deep learning algorithms. ICT Express, 4(4), 243–246. https://doi.org/10.1016/j.icte.2018.10.005

Townsend, K. L. (2024). One nervous system: Critical links between CNS and peripheral health. Diabetes, 73(12), 1967–1975. https://doi.org/10.2337/dbi24-0004

Zanelli, S., Ammi, M., Hallab, M., & El Yacoubi, M. A. (2022). Diabetes detection through PPG and ECG signals: A systematic review. Sensors, 22(13), 4890. https://doi.org/10.3390/s22134890

Zsombok, A., Desmoulins, L. D., & Derbenev, A. V. (2024). Sympathetic circuits regulating hepatic glucose metabolism. Physiological Reviews, 104(1), 85–101. https://doi.org/10.1152/physrev.00005.2023

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Published

2026-02-16

How to Cite

Coronado-Reyes, O. I., Tellez-Anguiano, A. del C., García-Rodríguez, E., Castro-Pimentel, L. A., & Olivares-Rojas, J. C. (2026). Design and Evaluation of a Third-Order Regression Model for Estimating Glucose Levels from ECG Signal Features. International Journal of Combinatorial Optimization Problems and Informatics, 17(2), 426–436. https://doi.org/10.61467/2007.1558.2026.v17i2.1110

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Vol. 17 No. 2 (2026)

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